How Deferred Deep Links Work on iOS and How to Integrate Them

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iOS Universal Links only work when an app is already installed, routing users via NSUserActivity — but they cannot carry link data across a fresh install. For users who tap a link, visit the App Store, and install the app, a deferred deep link mechanism is required, handled server-side and fetched on first launch. The LinkTrail SDK addresses both scenarios, offering installation via CocoaPods or Swift Package Manager and configuration through Xcode's Associated Domains capability. Developers must correctly set up an Apple App Site Association file — including the full Team ID prefix, a common source of silent failures — and call the appropriate lifecycle method to resolve incoming links. A separate first-open API call handles the deferred case, using probabilistic matching to route new users to the correct in-app destination.

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WClickHouse Adds Zero-Copy Apache Arrow Support for Faster Python Analytics

WClickHouse, an open-source Python library, has introduced zero-copy columnar data transfer between Apache Arrow and ClickHouse via new insert_arrow() and query_arrow() methods. The integration eliminates row-by-row serialization, a major bottleneck in Python analytics pipelines that causes high RAM usage and garbage collector pauses. Data in Arrow or Parquet format can now be streamed directly to ClickHouse over a socket without duplication in Python's heap. The library is compatible with Python 3.9 through 3.14 and supports Apache Arrow alongside Pydantic v2. It has been tested against live ClickHouse instances with over 95% test coverage and is available on GitHub and PyPI.

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ProgrammingDEV Community ·

Developer Builds CLI Tool That Uses AI to Generate Git Commit Messages

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ProgrammingDEV Community ·

WClickHouse Adds Zero-Copy Arrow Integration for Faster Python Analytics

An open-source Python library called WClickHouse has introduced Apache Arrow-based zero-copy columnar data transfer to ClickHouse databases. The library's insert_arrow() and query_arrow() methods allow developers to stream millions of rows directly without Python row iteration or JSON serialization overhead. This approach addresses common bottlenecks in data engineering pipelines, including high RAM usage, garbage collection pauses, and slow object conversion. The project supports Python versions 3.9 through 3.14 and is built with Apache Arrow and Pydantic v2. It is available on both GitHub and PyPI and claims over 95% test coverage verified against live ClickHouse instances.

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ProgrammingDEV Community ·

AI Agent Circuit Breakers: How Runaway LLM Loops Are Draining Enterprise Budgets

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